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Series

CPS Theory

Computer Sciences

2018

Articles 1 - 4 of 4

Full-Text Articles in Engineering

Data-Driven Model Predictive Control Using Random Forests For Building Energy Optimization And Climate Control, Francesco Smarra, Achin Jain, Tullio De Rubeis, Dario Ambrosini, Alessandro D'Innocenzo, Rahul Mangharam Apr 2018

Data-Driven Model Predictive Control Using Random Forests For Building Energy Optimization And Climate Control, Francesco Smarra, Achin Jain, Tullio De Rubeis, Dario Ambrosini, Alessandro D'Innocenzo, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case ...


Data-Driven Switched Affine Modeling For Model Predictive Control, Francesco Smarra, Achin Jain, Rahul Mangharam, Alessandro D'Innocenzo Apr 2018

Data-Driven Switched Affine Modeling For Model Predictive Control, Francesco Smarra, Achin Jain, Rahul Mangharam, Alessandro D'Innocenzo

Real-Time and Embedded Systems Lab (mLAB)

Model Predictive Control (MPC) is a well-consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict the system’s behavior over a predictive horizon. However, building physics-based models for large-scale systems, such as buildings and process control, can be cost and time prohibitive. To overcome this problem we propose in this paper a methodology to exploit machine learning techniques (i.e. regression trees and random forests) in order to build a state-space switched affine dynamical model of a large scale system only using historical data. Finite Receding Horizon Control (RHC) setup using control-oriented data-driven ...


Parameter Invariant Monitoring For Signal Temporal Logic, Nima Roohi, Ramneet Kaur, James Weimer, Oleg Sokolsky, Insup Lee Apr 2018

Parameter Invariant Monitoring For Signal Temporal Logic, Nima Roohi, Ramneet Kaur, James Weimer, Oleg Sokolsky, Insup Lee

Departmental Papers (CIS)

Signal Temporal Logic (STL) is a prominent specification formalism for real-time systems, and monitoring these specifications, specially when (for different reasons such as learning) behavior of systems can change over time, is quite important. There are three main challenges in this area: (1) full observation of system state is not possible due to noise or nuisance parameters, (2) the whole execution is not available during the monitoring, and (3) computational complexity of monitoring continuous time signals is very high. Although, each of these challenges has been addressed by different works, to the best of our knowledge, no one has addressed ...


Parameter-Invariant Monitor Design For Cyber Physical Systems, James Weimer, Radoslav Ivanov, Sanjian Chen, Alexander Roederer, Oleg Sokolsky, Insup Lee Jan 2018

Parameter-Invariant Monitor Design For Cyber Physical Systems, James Weimer, Radoslav Ivanov, Sanjian Chen, Alexander Roederer, Oleg Sokolsky, Insup Lee

Departmental Papers (CIS)

The tight interaction between information technology and the physical world inherent in Cyber-Physical Systems (CPS) can challenge traditional approaches for monitoring safety and security. Data collected for robust CPS monitoring is often sparse and may lack rich training data describing critical events/attacks. Moreover, CPS often operate in diverse environments that can have significant inter/intra-system variability. Furthermore, CPS monitors that are not robust to data sparsity and inter/intra-system variability may result in inconsistent performance and may not be trusted for monitoring safety and security. Towards overcoming these challenges, this paper presents recent work on the design of parameter-invariant ...